Structural healing after rotator cuff repair: clinical predictors, biomarkers, prediction models, and readiness for precision care

Structural healing after rotator cuff repair is clinically important, yet individual healing trajectories remain difficult to predict, monitor, and translate into individualized care. Evidence spans routine clinical predictors, systemic metabolic biomarkers, quantitative imaging, tissue phenotypes, genetic variation, transcriptomic signatures, and artificial intelligence–based prediction models. However, prior reviews rarely distinguish these evidence objects or evaluate them according to intended clinical use and stage of validation. This focused Mini Review assesses readiness for precision care across five linked dimensions: measurement validity, context-specific clinical validity, added value beyond routine predictors, independent validation, and action or impact evidence. Current literature is dominated by prognostic associations and model-development studies, whereas routine clinical predictors remain the most mature benchmarks. Promising signals have emerged across modalities, but heterogeneous structural endpoints, inconsistent measurement procedures, limited external validation, and the absence of decision-linked impact studies continue to impede individualized care. The principal challenge is therefore not candidate scarcity, but conversion of associations into reproducible, transportable, and actionable evidence. By mapping each evidence class to prognosis, monitoring, diagnosis, or treatment-effect prediction, this review proposes a translational roadmap for biomarker-informed risk stratification, postoperative surveillance, and prospective evaluation of individualized management pathways.